Mean Square Value Research Articles

Recent advances for assessment of the condensed phase heat of formation of high-energy content organic compounds and ionic liquids (or salts) to introduce a new computer code for design of desirable compounds

The condensed phase heat of formation of organic compounds as well as ionic liquids or salts is an important thermodynamic parameter for the evaluation of their energy contents, performances, and sensitivities. For using these materials as components of fuels, propellants or explosives, chemical industries should produce the compounds with high positive heat of formation because they can provide high heat of combustion or detonation performance. In contrast, their thermodynamic stability may decrease with higher positive values in their heats of formation. Experimental values of the condensed phase heats of formation for high energy content-neutral and ionic compounds are scarce in the literature. This work reviews reliable models for prediction of the condensed phase heat of formation of important classes of organic and ionic liquid energetic compounds. A novel easy to handle and user-friendly computer code is introduced for prediction of the condensed phase heat of formation using suitable and reliable predictive methods. The reliability of the new computer code is checked for organic compounds containing energetic groups -O-O-, –N3, –ON=O, –NO2, -ONO2 and –NNO2 as well as high nitrogen content materials and imidazolium- or triazolium-based ionic liquids (or salts). The values of the root mean square (rms) deviations of the condensed phase heat of formation by the new computer code/ quantum mechanical approaches for 11 neutral energetic organic compounds, 14 high-N content compounds, and 72 imidazolium- and triazolium-based energetic ionic liquids or salts are 45.8/78.0, 79.4/105.3, and 39.8/318.7 kJ/mol, respectively. Thus, the reliability of the outputs the new computer code is higher than the computed results from complex quantum mechanical methods, especially for imidazolium- and triazolium-based energetic ionic liquids or salts, wherein they need high-speed computers, specific computer codes and expert users.

Requirements Justification to Digital Signal Synthesizers for Various Types of Radar Systems

The paper justifies the requirements for the level of distortion of radar stations with pulse and quasi-continuous radiation, built on the basis of digital signal synthesizers of four types: digital synthesizers of voltage counts and digital synthesizers of phase counts with uniform sampling, digital synthesizers of voltage counts and digital synthesizers of phase counts with uneven sampling. When building a radar master device, the question arises about choosing the type of digital signal synthesizer. The main initial criteria are the maximum operating range of the digital synthesizer and the level of in-band distortion. When choosing the type of digital signal synthesizer, you have to take into account a large number of factors, the main of which are the complexity of the execution of the digital readout shaper, the possibility of implementing a digital readout shaper with the required speed and number of digits [1, 2]. When making requirements for the total level of distortion, the criterion of acceptable reduction of the probability of correct detection in comparison with its potential value for a fixed probability of false alarm is used. Based on this criterion, in pulse radars, the maximum relative RMS value of the distortion of the intercorrelation function of a signal with angular modulation generated by a digital synthesizer should not exceed Dδx ≤ –(51...67) dB. In a radar with quasi-continuous radiation, the maximum relative mean-square value of the distortion of the autocorrelation function of the signal with angular modulation should not exceed Dδ ≤ –(80...120) dB. The number of bits of phase quantization, voltage and time delay compensation in digital signal synthesizers depends not only on the maximum relative RMS value of the distortion of the intercorrelation function, but also on the number of samples of the signal with angular modulation. Therefore, initially you need to select the reference frequency of the digital signal synthesizer, setting the type of modulation and the effective spectrum width of the signal with angular modulation based on the tactical and technical characteristics radar

Validation of the Traumatic Antecedents Questionnaire using item response theory.

BackgroundTraumatic Antecedents Questionnaire (TAQ) is a traumatic experience scale that measures exposure to traumatic events across four age periods. Although the TAQ has good convergent validity with other traumatic scales, the classification of the domains and the psychometric properties of the scale has not been verified.MethodsA total of 290 young adults completed the TAQ, and 156 participated in the retest. The number of trauma domains was determined using principal component analysis. Rasch model was used for verifying the items that each domain might represent in one common measurement.ResultsWhen scores were transformed as binary a code 0 and 1 from the original 4 categories, 8 domains were established consisting of Domestic violence, Sexual/other rare trauma, Incompetence, Caring family, Accidents to close person, Unstable caring environment, Safe environment, and Lack of sexual/extreme trauma. Most domains had acceptable psychometric properties with a mean‐square fit value within the range of 0.7–1.3. The Bland and Altman analysis suggest 98.7% of difference scores between test and retest were within ±2 standard deviations from the mean. TAQ severity showed a significant relationship with the multiplicity score of the Maltreatment and Abuse Chronology of Exposure scale (r = 0.677).ConclusionsThe newly proposed scoring system and 8 domains for the TAQ demonstrated excellent internal consistency, test–retest reliability, and validity. Further studies are needed to develop new items in domains with less than 5 items to improve the psychometric properties of the scale and to create a maltreatment domain that includes bullying items.

A Computational Analysis of the Factors Governing the Dynamics of α7 nAChR and Its Homologs

The α7 nicotinic acetylcholine receptor is a homopentameric ion channel from the Cys-loop receptor superfamily targeted for psychiatric indications and inflammatory pain. Molecular dynamics studies of the receptor have focused on residue mobility and global conformational changes to address receptor function. However, a comparative analysis of α7 with its homologs that cannot trigger channel opening has not been made so far. To identify the residues involved in α7 activation, we ran triplicate 500-ns molecular dynamics simulations with an α7 extracellular domain homology model and two acetylcholine-binding protein homologs. We tested the effect of ligand binding and amino acid sequence on the structure and dynamics of the three proteins. We found that mobile regions identified based on root mean-square deviation and root mean-square fluctuation values are not always consistent among the individual α7 extracellular domain simulations. Comparison of the replica-average properties of the three proteins based on dynamic cross-correlation maps showed that ligand binding affects the coupling between the C-loop and the Cys-loop, vestibular loop, and β1–β2 loops. In addition, the main-immunogenic-region-like domain of α7 went through correlated motions with multiple domains of the receptor. These correlated motions were absent or diminished in α7 homologs, suggesting a unique role in α7 activation.

Loss rate forecasting framework based on macroeconomic changes: Application to US credit card industry

A major part of the balance sheets of the largest U.S. banks consists of credit card portfolios. Hence, managing the charge-off rates is a vital task for the profitability of the credit card industry. Different macroeconomic conditions affect individuals’ behavior in paying down their debts. In this paper, we propose an expert system for loss forecasting in the credit card industry using macroeconomic indicators. We select the indicators based on a thorough review of the literature and experts’ opinions covering all aspects of the economy, consumer, business, and government sectors. The state of the art machine learning models are used to develop the proposed expert system framework.We develop two versions of the forecasting expert system, which utilize different approaches to select between the lags added to each indicator. Among 19 macroeconomic indicators that were used as the input, six were used in the model with optimal lags, and seven indicators were selected by the model using all lags. The features that were selected by each of these models covered all three sectors of the economy. Using the charge-off data for the top 100 US banks ranked by assets from the first quarter of 1985 to the second quarter of 2019, we achieve mean squared error values of 1.15E−03 and 1.04E−03 using the model with optimal lags and the model with all lags, respectively. The proposed expert system gives a holistic view of the economy to the practitioners in the credit card industry and helps them to see the impact of different macroeconomic conditions on their future loss.

Determination of the relative inclination and the viewing angle of an interacting pair of galaxies using Convolutional Neural Networks

ABSTRACT Constructing dynamical models for interacting galaxies constrained by their observed structure and kinematics crucially depends on the correct choice of the values of their relative inclination (i) and viewing angle (θ) (the angle between the line of sight and the normal to the plane of their orbital motion). We construct Deep Convolutional Neural Network (DCNN) models to determine the i and θ of interacting galaxy pairs, using N-body + smoothed particle hydrodynamics (SPH) simulation data from the GalMer data base for training. GalMer simulates only a discrete set of i values (0°, 45°, 75°, and 90°) and almost all possible values of θ values in the range, [−90°, 90°]. Therefore, we have used classification for i parameter and regression for θ. In order to classify galaxy pairs based on their i values only, we first construct DCNN models for (i) 2-class (i = 0 °, 45°) (ii) 3-class (i = 0°, 45°, 90°) classification, obtaining F1 scores of 99 per cent and 98 per cent respectively. Further, for a classification based on both i and θ values, we develop a DCNN model for a 9-class classification using different possible combinations of i and θ, and the F1 score was 97${{\ \rm per\ cent}}$. To estimate θ alone, we have used regression, and obtained a mean-squared error value of 0.12. Finally, we also tested our DCNN model on real data from Sloan Digital Sky Survey. Our DCNN models could be extended to determine additional dynamical parameters, currently determined by trial and error method.

Mean-Square Consentability of Multiagent Systems With Nonidential Channel Fading

In this article, we are concerned with the consensus problem of multiagent system where the channels from each agent's control input to the plant are fading nonidentically. Two problems have been extensively studied. One is the consentable problem when the current state information is available for the controller. The other is that the state information is delayed for the controller in which we propose a novel predictorlike control protocol. It is obtained that the multiagent system with system matrices <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$A=B=1$</tex-math></inline-formula> is mean-square consentable for both problems. In particular, the mean-square consensus value is the average of all agents’ initial states. Furthermore, for the multiagent system with general dynamics, sufficient conditions have been obtained in terms of two parameterized Riccati inequality/equation for both problems, respectively.

BaZrO3 stability under pressure: The role of nonlocal exchange and correlation

The ground-stale structure of BaZrO3 is experimentally known to be cubic down to absolute zero. However, there exist several measured properties and experimental characterizations that earlier computational works have failed to accurately describe and explain within this cubic symmetry. Among these properties and observations are the dielectric constant and the parallel mean-squared relative displacement value that tracks the fluctuations in distance for Ba-O atom pairs. Previous density-functional theory (DFT) studies have resolved the issue by assuming that BaZrO(3 )undergoes a phase transition from cubic to tetragonal I4/mcm symmetry, possibly while forming a glasslike state that reflects cubic symmetry on average. In this paper, we show that the set of experimental results can indeed be satisfactorily explained by DFT entirely within the cubic symmetry. We find that past theory limitations arose from the choice of exchange-correlation-functional approximations and that the inclusion of Fock exchange in hybrids significantly improves the DFT performance. We also find that the inclusion of nonlocal correlation effects is beneficial. We conclude by making a prediction for the phase-transition pressure for the transition from cubic to tetragonal symmetry at zero kelvin.

Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights

Nonresponse is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random nonresponse using auxiliary data. In this study, it is assumed that random nonresponse occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random nonresponse. In particular, auxiliary information is used via an improved Nadaraya–Watson kernel regression technique to compensate for random nonresponse. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of a finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at 95% coverage rate. The results obtained in this study are useful for instance in choosing efficient estimators of a finite population mean in demographic sample surveys.

ITEM ANALYSIS FOR THE MODIFIED VERSION OF ADOLESCENT PARENTING ATTITUDE FOUR FACTOR QUESTIONNAIRE USING RASCH MEASUREMENT MODEL

This study aims to conduct a validity and reliability test on the modified version of adolescent parenting attitude four factor questionnaire (APA-FFQ) with rasch measurement model. The adapted instrument consisted of 40 items based on 4 constructs namely authoritarian, authoritative, permissive and uninvolved parenting style. Some modifications have been carried out to improve the item clarity and to capture the type of maternal and paternal parenting style. Based on the sample size of 70 adolescents from one of the private higher education institution in Malaysia, the analysis revealed that all items from the modified version of APA-FFQ fits the model as the value of infor and outfit mean square (MNSQ) fell within the acceptable range of 0.50 to 1.50 with positive Point Measure Correlation (PTMEA) value. The dimensionality analysis also revealed that all items involved were measured what it supposed to measure and possessed a strong unidimensionality. Furthermore, the separation and reliability analysis showed that all items and respondents are stable and consistent when tested with different items in measuring the same construct. Therefore, the instrument can be adapted in the Malaysian educational context.

PREDICTION OF GLUCOSE LEVEL IN DIABETICS WITH SUPPORT VECTOR REGRESSION

One of the common diabetes factors that people hear is that they consume too much or often consume sweet foods or drinks so that blood sugar in the human body increases. The times and increasingly sophisticated technology make it easier for someone to be able to predict a disease such as diabetes with machine learning techniques. Therefore, from the existing problems, a machine learning technique will be made in predicting glucose levels in diabetics. The aim is to predict glucose levels in diabetics and find the best algorithm from several comparison algorithms. The results of the experiments carried out by the support vector regression algorithm have a lower mean squared error value of 28.9480 compared to other comparative algorithms and visualize the error classification seen that Instance no 47 has a prediction of the highest plasma glucose value of 189.2305.

Modeling the emergence of North African knapweed (Centaurea diluta), an increasingly troublesome weed in Spain

AbstractNorth African knapweed (Centaurea diluta Aiton) is an annual weed that is widespread in southern Spain and is of increasing concern in dryland cropping systems. Despite its expanding range in Spain, there is limited information on the emergence timing and pattern of this species, knowledge of which is critical for developing more timely and effective management strategies. Therefore, there is a need to develop simple and reliable models to predict the timing and emergence of this annual weed under dryland conditions. A multi-location field experiment was established across Spain in 2016 to 2017 to assess the emergence of C. diluta. At each of 11 locations, seeds were sown in the fall, and emergence was recorded. Overall emergence averaged 39% in the first year across all sites and 11% in the second year. In both years, the main emergence flush occurred at the beginning of the growing season. A three-parameter Weibull function best described seedling emergence of C. diluta. Emergence models were developed based on thermal time (TT) and hydrothermal time (HTT) and showed high predictability, as evidenced by root mean-square error prediction values of 10.8 and 10.7, respectively. Three cardinal points were established for TT and HHT at 0.5, 10, and 35 C for base, optimal, and ceiling temperatures, respectively, while base water potential was estimated at −0.5 MPa.

Bayesian cluster hierarchical model for subgroup borrowing in the design and analysis of basket trials with binary endpoints.

Master protocol designs are often proposed to improve the efficiency of drug development with multiple subgroups. In the basket trial design, different subgroups can have similar biological pathogenesis pathways. Hence, a target therapy can result in similar responses. A good information sharing strategy between different subgroups can potentially improve the efficiency of evaluating treatment efficacy. In traditional hierarchical models, based on the exchangeability assumption, all subgroups are placed into the same sharing pool for cross subgroup information sharing. However, due to the heterogeneity between subgroups, there can be large differences in drug efficacy. Under such cases, strong borrowing across all subgroups is not suitable and no borrowing can be inefficient, because the treatment effect is analyzed in each subgroup separately. We propose a Bayesian cluster hierarchical model (BCHM) to improve the operating characteristics of estimating the treatment effect in multiple subgroups in basket trials. Bayesian nonparametric method is applied to dynamically calculate the number of clusters by conducting a multiple cluster classification based on subgroup outcomes. A hierarchical model is used to compute the posterior probability of the treatment effect, with the borrowing strength determined by the Bayesian nonparametric clustering and the similarities between subgroups. We apply the BCHM to clinical trials with binary endpoints. For treatment effect estimation, the BCHM yields lower mean squared error values, when compared to the independent analyses. In scenarios with a heterogeneous treatment effect, the BCHM provides lower mean squared error values compared to traditional hierarchical models. In addition, we can construct a loss function to optimize the design parameters. BCHM provides a balanced approach and smart borrowing, which yields better results in assessing the treatment effect in different scenarios compared to other conventional methods.

Identification of Body Balance Deterioration of Gait in Women Using Accelerometers

This study presents a simple methodology for the evaluation of gait with accelerometers, for rapid and simple application, in which we employ current balance tests in clinical practice (Timed Up and Go, Chair Stand Test and Six-Minute Walk Test). The aim was to determine whether the accelerometric valuation of gait can detect alterations in balance. The sample of this cross-sectional research, made during the months of May and June 2018, was composed of 145 healthy adult women (x = 63.8 ± 8.41 years), from the city of Ourense (Spain). They walked with a triaxial accelerometer at the fourth lumbar vertebra for a distance of 20 m. The test was repeated three times, and the mean of the three measurements was used in the analysis. There was a reduction in the values of acceleration recorded along all three of the axes and in the root mean square as the age increased. This reduction was very significant for the minimum values registered along the vertical and transverse axes, and for the maximum values along the mediolateral axis. Only the maximum values of the vector module demonstrated significant differences among the three age groups. A regression model allowed us to identify the values that give more information on the Timed Up and Go Test, namely: the maximum values of the root mean square and the mediolateral axis. An exhaustive analysis of the vertical and mediolateral axes and the vector module allows for the detection of early alterations in the automatic gait pattern.

Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks.

In this paper, the authors used an acoustic wave acting as a disturbance (acoustic vibration), which travelled in all directions on the whole surface of a dried strawberry fruit in its specified area. The area of space in which the acoustic wave occurs is defined as the acoustic field. When the vibrating surface—for example, the surface of the belt—becomes the source, then one can observe the travelling of surface waves. For any shape of the surface of the dried strawberry fruit, the signal of travelling waves takes the form that is imposed by this irregular surface. The aim of this work was to research the effectiveness of recognizing the two trials in the process of convection drying on the basis of the acoustic signal backed up by neural networks. The input variables determined descriptors such as frequency (Hz) and the level of luminosity (dB). During the research, the degree of crispiness relative to the degree of maturity was compared. The results showed that the optimal neural model in respect of the lowest value of the root mean square turned out to be the Multi-Layer Perceptron network with the technique of dropping single fruits into water (data included in the learning data set Z2). The results confirm that the choice of method can have an influence on the effectives of recognizing dried strawberry fruits, and also this can be a basis for creating an effective and fast analysis tool which is capable of analyzing the degree of ripeness of fruits including their crispness in the industrial process of drying fruits.

Heterosis, Combining Ability and Gene Action for Yield in Bottle Gourd

The experiment was conducted at the experimental field of Olericulture Division, Horticulture Research Centre (HRC), Bangladesh Agricultural Research Institute (BARI), Gazipur, Bangladesh during the winter season of 2018-2019 to study the genetic architecture of yield in a seven parent half diallel cross of bottle gourd. The values of mean square for GCA (general combining ability) and SCA (specific combining ability) were highly significant which suggested the presence of both additive and non-additive genetic variance in the population. But the higher magnitude of GCA compared to SCA indicated predominance of additive genetic variance. In most of the cases, the cross between poor and poor parents showed positive SCA effect for fruit yield, which indicated the higher yield. The estimates of significant positive better parent heterosis ranged from 6.27 to 49.72 percent. Analysis of genetic components of variation suggested that additive components were more important in the inheritance of fruit yield. This character was observed being controlled by two to three pairs of genes or groups of genes. Narrow sense heritability was 23 percent indicating probability of selection in generations. The graphical analysis also indicated wide genetic diversity among the parents.

Can Virtual Reality be used as a significant stressor for studies using ECG?

In previous studies, we used a method for detecting stress that was based exclusively on heart rate and ECG for differentiation between such situations as mental stress, physical activity, relaxation, and rest. As a response of the heart to these situations, we observed different behavior in the Root Mean Square of the Successive differences heartbeats (RMSSD). This study aims to analyze Virtual Reality via a virtual reality headset as an effective stressor for future works. The value of the Root Mean Square of the Successive Differences is an important marker for the parasympathetic effector on the heart and can provide information about stress. For these measurements, the RR interval was collected using a breast belt. In these studies, we can observe the Root Mean Square of the successive differences heartbeats. Additional sensors for the analysis were not used. We conducted experiments with ten subjects that had to drive a simulator for 25 minutes using monitors and 25 minutes using virtual reality headset. Before starting and after finishing each simulation, the subjects had to complete a survey in which they had to describe their mental state. The experiment results show that driving using virtual reality headset has some influence on the heart rate and RMSSD, but it does not significantly increase the stress of driving.

Detecting pneumatic actuator leakage using acoustic emission monitoring

This paper focuses on the development of an efficient new diagnostic procedure for checking the function of pneumatic cylinders using acoustic emission. This diagnostic procedure is able to detect distinctive differences that determine whether the cylinder is damaged or undamaged. Moreover, the paper aims to find the diagnostic criteria that can be used to evaluate the pneumatic cylinder and detect defects. In this study, acoustic emission testing of several undamaged cylinders is carried out before artificial defects are created in each one. The signals from the progress and retreat strokes are recorded and analysed according to many parameters. The root mean square is normalised and the different responses of damaged and undamaged pneumatic cylinders are recognised by the time delay of the strokes. The differences are identified by comparing the maximum root mean square from sensor A and the maximum root mean square from sensor B for one cycle in the retreat stroke. The damaged and undamaged cylinders are distinguished using the difference in energy values present in the signals of the two sensors in the retreat stroke. The final evaluation of the cylinder is determined by calculating the total value of the root mean square. This paper is a continuation of a prior article and the extension of that work.

Effect of tire dynamic characteristics on vibration load at the operator’s workplace

The article addresses a current issue – reducing the vibration load at the operator’s workplace in the utility machine with respect to the use of chassis elements as vibroprotection element. The results of studies aimed at determining the effect of tires dynamic characteristics on the vibration load of the operator’s workplace are presented. A computational scheme designed to conduct experimental research has been shown. Correlation between tire rigidity and air pressure in the tire, as well as correlation between tire service life and tire pressure have been revealed. Results of studies aimed at determining relations between the dynamics of mean-square values of vibration acceleration of the frame, the floor and the operator’s seat, and the rigidity coefficients of utility machine tires. Conclusions have been made as to the relevance of using chassis elements as vibroprotection element at the operator’s workplace in the utility machine.

The clinical research progress of neck muscle tensity detection methods and evaluation index

Cervical muscle group has complex anatomical and physiological characteristics, which plays an important role in maintaining cervical stability and participating in cervical activity. Long-term bow of the head will cause irreversible injury to the neck muscle. The clinical manifestations are stiffness of the neck, and increased muscle tension. In severe cases, muscle spasms are like stripes. The pain caused by it for more than 3 months is called chronic neck pain(CNP). With the increasing incidence of chronic neck pain in the population, the related case reports and clinical studies are also increasing. At present, it is generally believed that persistent non-autonomous hypertension of neck muscle is the primary pathological mechanism of chronic neck pain. For the objective, quantitative analysis and evaluation of neck muscle tension is helpful to predict the occurrence of chronic neck pain, explain its pathogenesis and formulate treatment measures, which has gradually become the main development trend in this field. In recent years, with the development of auxiliary medicine, evaluation methods have changed with each passing day, which provides a variety of options for clinical research. In this paper, several mainstream detection methods of cervical muscle tension are summarized, and the shear wave elastography technology, surface electromyography, magnetic resonance imaging and diffusion tensor imaging are introduced respectively. Their development history, and the working principle of each technology aredescribed in detail. By reviewing the classical clinical research cases at domestic and abroad, the research status of each technology in the field of cervical muscle detection is deeply analyzed. The development trend, potential advantages and limitations of Yang's modulus, integrated electromyography, root mean square value, mean power frequency, median frequency, cross sectional area and apparent diffusion coefficient were evaluated. The purpose of this paper is to summarize experience, guide clinic, deepen the understanding of microcosmic pathogenesis of chronic neck pain, provide theoretical basis for exploring new detection methods, provide omni-directional, multi-level and three-dimensional new clinical diagnosis and treatment strategies for related fields, and provide new methods and ideas for the follow-up study of chronic neck pain.